Job Description

ABOUT GREYSTAR

Greystar is a leading, fully integrated global real estate platform offering expertise in property management, investment management, development, and construction services in institutional-quality rental housing. Headquartered in Charleston, South Carolina, Greystar manages and operates over $300 billion of real estate in over 260 markets globally with offices throughout North America, Europe, South America, and the Asia-Pacific region. Greystar is the largest operator of apartments in the United States, managing more than one million units/beds globally. Across its platforms, Greystar has over $79 billion of assets under management, including approximately $36 billion of development assets and over $30 billion of regulatory assets under management. Greystar was founded by Bob Faith in 1993 to become a provider of world-class service in the rental residential real estate business. To learn more, visit www.greystar.com

Greystar’s D2AI team is responsible for the platforms, processes, and practices that power AI across the organization. This role goes beyond traditional delivery—your decisions influence how data is transformed into intelligent, scalable solutions used by teams company‑wide. We require AI fluency because this role sits at the intersection of data, technology, and business outcomes. That means understanding how AI systems are designed and operationalized, using AI‑enabled tools in day‑to‑day work, and partnering effectively with engineering, analytics, and business teams to ensure AI solutions are reliable, responsible, and impactful.

In addition to your resume, all candidates are required to include a short video (2–5 min) demonstrating how you've used AI to improve your work — analysis, research, writing, process automation, or decision support. We recommend recording with Loom (free) or uploading as an unlisted YouTube video.

Please embed this link at the top of your resume. Applications without a video link will not be reviewed.

GPS is Greystar’s asset performance platform — the system through which we measure, compare, and optimize performance across the world’s largest multifamily portfolio. We are building GPS into a true data science platform: one that doesn’t just report on the past, but predicts outcomes, prescribes actions, and sets the industry standard for how asset performance is understood and improved.

We’re seeking a Senior Manager, Data Science to lead the data science function embedded within the GPS product team. This is a hands-on leadership role: you will build models, lead a small team of data scientists and ML engineers, and work directly with Product, Engineering, and business stakeholders to ship intelligence into a product used by Greystar’s leadership, property managers, owners, and investors. Our team includes engineers, designers, and product leaders with experience from Google, Microsoft, Airbnb, Strava, Amazon, and more.

KEY RESPONSIBILITIES:

Build and Ship Predictive and Prescriptive Models

• Design, build, and deploy models that power GPS — including asset performance scoring, NOI forecasting, renewal probability, lease pricing optimization, and anomaly detection across portfolio KPIs.
• Develop benchmarking and comparative analytics capabilities that allow Greystar to measure any asset against the portfolio, the market, and historical performance.
• Build models that move GPS from descriptive reporting toward prescriptive intelligence: surfacing what to do, not just what happened.
• Own the full model lifecycle within GPS: problem framing, feature engineering, model development, validation, deployment, monitoring, and iteration.

Integrate AI into the GPS Product Experience

• Partner tightly with GPS Product and Engineering to embed data science outputs directly into the product — as scores, alerts, recommendations, and natural language insights.
• Design and implement LLM-powered features within GPS, including AI-generated asset summaries, conversational data exploration, and automated narrative reporting for owners and investors.
• Build and maintain feature stores and serving infrastructure that allow models to operate in real time within the GPS application.
• Define and track model performance metrics that tie directly to business outcomes: accuracy of forecasts, adoption of recommendations, and measured NOI impact.

Lead and Grow a Data Science Team

• Manage and mentor a team of 2–4 data scientists and ML engineers embedded in the GPS product organization.
• Set technical standards for the team: code quality, model documentation, reproducibility, and peer review.
• Prioritize the data science backlog in partnership with Product Management, balancing quick wins with longer-horizon model development.
• Recruit and retain top data science talent; build a team culture that values shipping over research and business impact over model complexity.

Partner Across the Organization

• Work with Data Engineering to ensure GPS has access to clean, validated, timely data from the DMP and upstream source systems.
• Collaborate with the Chief Data Scientist (Global) on methodology, model governance, and reusable frameworks that serve both GPS and other business units.
• Translate business questions from USPM, Investment, and Development stakeholders into data science problems that GPS can solve at scale.
• Present model results, insights, and recommendations to senior business leaders including GPS product reviews and executive stakeholder meetings.

BASIC KNOWLEDGE & QUALIFICATIONS:

Data Science Expertise

• 7+ years of applied data science or ML engineering experience, with at least 2 years managing a data science team.
• Strong track record of building and deploying models in production — not just notebooks and prototypes, but models that drive real product features and business decisions.
• Deep expertise in supervised/unsupervised learning, time series forecasting, regression, classification, and anomaly detection.
• Strong Python skills and fluency with the modern ML stack: scikit-learn, XGBoost/LightGBM, PyTorch or TensorFlow, MLflow or similar experiment tracking.
• Experience with feature engineering at scale, including working with large, messy, real-world datasets.

Applied AI and LLM Experience

• Hands-on experience with LLMs in production: prompt engineering, fine-tuning, RAG, structured output generation, and evaluation frameworks.
• Understanding of how to integrate AI/ML outputs into product UX — not just model accuracy, but how predictions are presented, explained, and acted upon by users.
• Awareness of model governance: bias detection, fairness, explainability, and responsible AI practices.

Product and Business Orientation

• Experience working as an embedded data scientist within a product team, not a centralized research group.
• Ability to translate ambiguous business problems into structured data science work with clear success criteria.
• Comfort presenting to non-technical senior stakeholders and making the case for data science investments in business terms.

Domain Knowledge (Preferred)

• Experience in real estate, property management, asset management, or financial services is a strong plus.
• Familiarity with NOI drivers, lease economics, occupancy modeling, or real estate valuation concepts.
• Experience with benchmarking, scoring, or index-building methodologies.

Tools & Technologies

• Python, SQL, Databricks/Spark for model development at scale.
• MLflow, Weights & Biases, or similar for experiment tracking and model registry.
• Azure ML, SageMaker, or similar cloud ML platforms.
• LLM APIs (OpenAI, Anthropic), vector databases, and retrieval frameworks.
• Git, CI/CD for ML pipelines, and infrastructure as code

TRAVEL / PHYSICAL DEMANDS:

• Team members work in an office or remote work environment. No special physical demands are required.
• Rare or occasional travel may be required to attend business meetings, training programs, or other situations necessary for the accomplishment of some or all of the daily responsibilities of this position.

The salary range for this position is $140,000-$170,000 USD Annually.

#LI-BB1

#LI-Remote

Additional Compensation

Many factors go into determining employee pay within the posted range including business requirements, prior experience, current skills and geographical location.

• Corporate Positions In addition to the base salary, this role may be eligible to participate in a quarterly or annual bonus program based on individual and company performance.

• Onsite Property Positions In addition to the base salary, this role may be eligible to participate in weekly, monthly, and/or quarterly bonus programs.

Robust Benefits Offered*:

• Competitive Medical, Dental, Vision, and Disability & Life insurance benefits. Low (free basic) employee Medical costs for employee-only coverage; costs discounted after 3 and 5 years of service.

• Generous Paid Time off. All new hires start with 15 days of vacation, 4 personal days, 10 sick days, and 11 paid holidays. Plus your birthday off after 1 year of service! Additional vacation accrued with tenure.

• For onsite team members, onsite housing discount at Greystar-managed communities are available subject to discount and unit availability.

• 6-Week Paid Sabbatical after 10 years of service (and every 5 years thereafter).

• 401(k) with Company Match up to 6% of pay after 6 months of service.

• Paid Parental Leave and lifetime Fertility Benefit reimbursement up to $10,000 (includes adoption or surrogacy).

• Employee Assistance Program.

• Critical Illness, Accident, Hospital Indemnity, Pet Insurance and Legal Plans.

• Charitable giving program and benefits.

*Benefits offered for full-time employees. For Union and Prevailing Wage roles, compensation and benefits may vary from the listed information above due to Collective Bargaining Agreements and/or local governing authority.

Greystar will consider for employment qualified applicants with arrest and conviction records.

Important Notice: Greystar will never request your banking details or other sensitive personal information during the interview process. Greystar does not conduct any interviews via text or messaging, and all communication will come from official Greystar email addresses (@greystar.com). If you receive suspicious requests, please report them immediately to AskHR@greystar.com.

ANTICIPATED CLOSING DATE

May 31, 2026

This date may be subject to change due to evolving business needs.